lilt-roBERTa-en-base-sroie

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0348
• Address: {'precision': 0.92, 'recall': 0.9279538904899135, 'f1': 0.9239598278335724, 'number': 347}
• Company: {'precision': 0.9405099150141643, 'recall': 0.9567723342939481, 'f1': 0.9485714285714285, 'number': 347}
• Date: {'precision': 0.9827586206896551, 'recall': 0.9855907780979827, 'f1': 0.9841726618705036, 'number': 347}
• Total: {'precision': 0.9131652661064426, 'recall': 0.9394812680115274, 'f1': 0.9261363636363636, 'number': 347}
• Overall Precision: 0.9389
• Overall Recall: 0.9524
• Overall F1: 0.9456
• Overall Accuracy: 0.9954

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Address	Company	Date	Total	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0536	6.3291	500	0.0261	{'precision': 0.9067796610169492, 'recall': 0.9250720461095101, 'f1': 0.9158345221112697, 'number': 347}	{'precision': 0.9273743016759777, 'recall': 0.9567723342939481, 'f1': 0.9418439716312057, 'number': 347}	{'precision': 0.9828080229226361, 'recall': 0.9884726224783862, 'f1': 0.985632183908046, 'number': 347}	{'precision': 0.883008356545961, 'recall': 0.9135446685878963, 'f1': 0.8980169971671388, 'number': 347}	0.9246	0.9460	0.9352	0.9949
0.0058	12.6582	1000	0.0326	{'precision': 0.9176136363636364, 'recall': 0.930835734870317, 'f1': 0.9241773962804005, 'number': 347}	{'precision': 0.9323943661971831, 'recall': 0.9538904899135446, 'f1': 0.9430199430199431, 'number': 347}	{'precision': 0.9827586206896551, 'recall': 0.9855907780979827, 'f1': 0.9841726618705036, 'number': 347}	{'precision': 0.8910081743869209, 'recall': 0.9423631123919308, 'f1': 0.9159663865546217, 'number': 347}	0.9304	0.9532	0.9416	0.9950
0.0019	18.9873	1500	0.0348	{'precision': 0.92, 'recall': 0.9279538904899135, 'f1': 0.9239598278335724, 'number': 347}	{'precision': 0.9405099150141643, 'recall': 0.9567723342939481, 'f1': 0.9485714285714285, 'number': 347}	{'precision': 0.9827586206896551, 'recall': 0.9855907780979827, 'f1': 0.9841726618705036, 'number': 347}	{'precision': 0.9131652661064426, 'recall': 0.9394812680115274, 'f1': 0.9261363636363636, 'number': 347}	0.9389	0.9524	0.9456	0.9954

Framework versions

• Transformers 4.42.3
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1